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Organizations struggle to manage unforeseen challenges without external assistance, due to the rapidly changing environment and complex organizational structures (Terjesen & Patel, 2017). Obtaining knowledge from external sources can reduce uncertainties and enhance decision-making quality (Gupta & George, 2016). Evolutionary economists have highlighted the significance of external knowledge in identifying valuable information sources. However, the complex characteristics of organizational knowledge, such as hiddenness, competition and indivisibility, pose substantial challenges to effective knowledge search activities. The usage of cutting-edge search approaches and technologies can combine and assess massive amounts of irregularly distributed external information, which can promote communication and knowledge exchange, particularly in remote work contexts, thereby accelerating internal knowledge sharing (Nejatian et al., 2013). Multiple studies have demonstrated that strategies applied in seeking and acquiring information significantly influence organizational innovation and productivity (Matricano et al., 2019; Papa et al., 2020; Wang et al., 2020). Moreover, exploring external knowledge can enrich an organizations' knowledge bank, enabling the identification and utilization of additional external resources (Ren et al., 2015). Thus, developing and implementing effective knowledge search strategies are crucial for organizations to incorporate novel ideas and insights.
Prior studies have extensively examined how external knowledge impacts organizational innovation performance and business models, focusing on the way of knowledge acquisition and their consequences. In the race of innovation and productivity, harnessing organizational information technology (IT) resources to their fullest potential is imperative for promoting organizational learning and effective knowledge management (Andreu & Ciborra, 1996; Kane & Alavi, 2007; Nguyen et al., 2019; Soto-Acosta et al., 2018). Evidence has indicated that contextual clues are invaluable in external investigations, playing a pivotal role in information retrieval, ubiquitous computing, data mining, and website recommendations. The advent of the Internet of Things (IoT) has enabled the generation of accurate, real-time predictions and informed decisions based on contextual clues. Context-aware recommendation systems provide users with personalized predictions by utilizing their preferences and activities to build powerful context-aware machine learning models, which greatly improves users’ daily experiences (Sarker et al., 2020). Context-aware recommendation systems, leveraging intelligent data in diverse environments, significantly influence users’ decision-making processes and enhance the efficacy of collaborative filtering (CF) (Liu, X., et al., 2020). Employing these innovative technologies for knowledge management is essential for organizations aiming to maintain their competitive advantage and achieve sustained success.
The paper is organized as follows: The theoretical background section underscores the direct impacts of different knowledge search strategies—search depth and search breadth—on knowledge integration, alongside the influence of knowledge integration on decision quality. It then examines the potential moderating role of context-aware recommendations on these dynamics, laying the groundwork for our hypotheses. Subsequent sections describe the research methodology, including survey design and participant demographics, followed by a presentation of the empirical findings. The discussion synthesizes these results, highlighting key insights, theoretical contributions, managerial implications, and directions for future research.